Robot and its control method

ABSTRACT

In a robot and its control method the behavior and motion models functioning as bases in generating behaviors and motions of the robot are transformed based on the input history from the outside and the history of its own behaviors and motions.

TECHNICAL FIELD

[0001] The present invention relates to a robot and its control method,and is suitably applied to a pet robot.

BACKGROUND ART

[0002] Of late a 4-legged walking pet robot has been proposed anddeveloped by this patent applicant, which acts according to thedirections from a user and surroundings it is put in. Such a pet robottakes the shape of a dog or cat kept in an ordinary home and actsautonomously according to the directions from the user and surroundingsit is put in. A set of motions is defined as ‘behavior’ in the followingexplanation.

[0003] It is imagined that if such a pet robot is provided with thefunction of ‘growing’ like a real dog or cat, the user may get a muchlarger sense of affinity and satisfaction out of the pet robot, therebyincreasing the amusement of the pet robot.

[0004] With the pet robot provided with the ‘growth’ function if somemore innovative contrivance is incorporated into it, that is capable ofretaining the user's interest and precluding the user getting tired ofthe pet robot's behaviors and motions in such a case that the pet robotceases to grow any longer or that it takes a long period of time untilit resumes growing, much more amusement may be induced out of the petrobot.

DISCLOURE OF THE INVENTION

[0005] The present invention has been made considering the foregoing andintends to offer a robot and control method for it which may increasethe amusement on the part of the user.

[0006] In order to solve the subject matter, with the present inventionthe robot is provided with a behavior and/or motion generation means forgenerating behaviors and/or motions based on the behavior and/or motionmodels, and a behavior and/or motion model transforming means fortransforming behavior and/or motion models into a higher level ofbehavior and/or motion models at a given timing based on at least one ofthe input history from the outside and the behavior and/or motionhistory of its own, so that the behaviors and motions are transformed asif a pet robot were ‘growing’. Thus a robot can be realized, whoseamusement quality (entertainingness) is greatly increased for the user.

[0007] Also, with the present invention the robot is provided with abehavior and/or motion generation means for generating behaviors and/ormotions based on the behavior and motions models, and a firsttransforming means for transforming behaviors and/or motions into ahigher level of behaviors and/or motions in order, according to a set offirst given conditions, and a second transforming means for transformingbehaviors and/or motion models into the same or lower level of behaviorsand/or motions, according to a set of second given conditions based onat least one of the input history from the outside and behaviors and/ormotions history of its own, so that the behaviors and motions of therobot are prevented from being tired of. Thus a robot can be realized,whose amusement quality (entertainingness) is further increased for theuser.

[0008] Furthermore, with the present invention the robot is providedwith a behavior and/or motion generation means for generating behaviorsand/or motions based on the behavior and/motion models, and a behaviorand/or motion transforming means for transforming behaviors and/ormotions into behavior and/or motion models of a higher growth level at agiven timing based on the evaluation results obtained by evaluating itsown behaviors in accordance with given evaluation functions. In thismanner the behaviors and motions can be transformed as if the robot weregrowing. Thus a robot can be realized whose amusement quality(entertainingness) is greatly increased for the user.

[0009] Furthermore, with the present invention the robot having aplurality of behavior and/or motion models for a plurality of behaviorsand/or motions, is provided with a behavior and/or motion generationmeans for generating behaviors and/or motions based on the behaviorand/or motion models of corresponding behavior patterns, and atransforming means for transforming each behavior and/or motion model ofthe corresponding behavior pattern, with the use of which each behaviorand/or motion model of the corresponding behavior pattern can betransformed with different regulations preset for each behavior and/ormotion model, so that the individuality of the robot can be diversified.Thus a robot can be realized whose amusement quality (entertainingness)is greatly enhanced.

[0010] Furthermore, with the present invention the control method for arobot comprises the first step wherein behaviors and/or motions aregenerated based on the behavior and/or motion models, and the secondstep where behavior and/or motion models are transformed into behaviorand/or motions models of a higher level at a given timing based on atleast one of the input history from the outside and behavior and/ormotion history of its own, so that behaviors and motions can betransformed as if the robot were growing. Thus a control method can berealized, due to which the amusement quality (entertainingness) of therobot is substantially enhanced.

[0011] Furthermore, with the present invention the control method for arobot comprises the first step where behaviors and/or motions aregenerated based on the behavior and/or motion models, and the secondstep wherein behavior and/or motion models are transformed into behaviorand/or motion models of a higher growth level based on the first givenconditions, and wherein behavior and/or motion models are transformedinto behavior and/or motion models of an equal or a lower growth levelbased according to second given conditions based on at least one of theinput history from the outside and behavior and/or motion history of itsown, so that the behaviors and motions of a robot are effectivelyprevented from being tired of. Thus a control method can be realized,which increases the amusement of a robot substantially.

[0012] Furthermore, with the present invention the control method for arobot comprises the first step wherein behaviors and/or motions aregenerated based on the behavior and motion models and the second stepwherein behaviors and/or motions are transformed into behavior andmotion models of a higher growth level at a given timing based on theevaluation results obtained by evaluating its own behaviors inaccordance with given evaluation functions, so that the behaviors andmotions are transformed as if a robot were growing. Thus the controlmethod for a robot can be realized, whose entertainingness issubstantially enhanced.

[0013] Furthermore, with the present invention the control method for arobot having a plurality of behavior and/or motion models for aplurality of behavior patterns, comprises the first step whereinbehaviors and motions are generated based on each behavior and/or motionof the corresponding behavior pattern, and the second step where eachbehavior and/or motion model of the corresponding behavior pattern istransformed responding to the influence from the outside, and whereineach behavior and/or motion model of the corresponding behavior patternis transformed according to a different regulation preset for eachbehavior and/or motion model, so that the individuality of the robot canbe diversified. Thus the control method for a robot can be realized thatincreases the amusement quality (entertainingness) greatly.

BRIEF DESCRIPTION OF THE DRAWINGS

[0014]FIG. 1 is a perspective view of the external structure of a petrobot embodying the present invention.

[0015]FIG. 2 is a block diagram of the circuit configuration of a petrobot.

[0016]FIG. 3 is a conceptual chart of growth models.

[0017]FIG. 4 is block diagram instrumental in describing the processesof a controller.

[0018]FIG. 5 is a conceptual diagram instrumental in describing a dataprocessing in a feeling/instinct modeling unit.

[0019]FIG. 6 is a conceptual diagram of probability automaton.

[0020]FIG. 7 is a conceptual table of a state transition.

[0021]FIG. 8 is a conceptual diagram instrumental in describing adirected graph.

[0022]FIG. 9 is a conceptual diagram of the directed graph for the wholebody.

[0023]FIG. 10 a conceptual diagram of a directed graph for the head.

[0024]FIG. 11 is a conceptual diagram of a directed graph for the legs.

[0025]FIG. 12 is a conceptual diagram of a directed graph for the tail.

[0026]FIG. 13 is a conceptual diagram of a first growth element list andfirst growth element counter table.

[0027]FIG. 14 is a conceptual diagram of a second growth element listand second growth element counter table.

[0028]FIG. 15 is a flowchart of a growth control processing procedure.

[0029]FIG. 16 is a conceptual diagram of other embodiments.

[0030]FIG. 17 is a conceptual diagram of other embodiments.

[0031]FIG. 18 is a conceptual diagram of growth models in a secondembodiment.

[0032]FIG. 19 is a block diagram instrumental in describing theprocessing of the controller in the second embodiment.

[0033]FIG. 20 is a conceptual diagram instrumental in describing theacquisition and lapse of memory of the behavior patterns attendant upongrowth.

[0034]FIG. 21 a conceptual diagram instrumental in describingdifferential files.

[0035]FIG. 22 is a conceptual diagram instrumental in describing atransforming method for behavior and motion models.

[0036]FIG. 23 is a conceptual diagram instrumental in describing otherembodiments.

[0037]FIG. 24 is a conceptual diagram of behavior pattern transformingand retrogressive models in a third embodiment.

[0038]FIG. 25 a block diagram instrumental in describing the processingof the controller in the third embodiment.

[0039]FIG. 26 is a conceptual diagram of a first behavior patternelement list and first behavior pattern element counter table.

[0040]FIG. 27 is a conceptual diagram of a second behavior patternelement list and second behavior pattern element counter table.

[0041]FIG. 28 is a conceptual diagram of a retrogressive element listand retrogressive element counter table.

[0042]FIG. 29 is a conceptual diagram of a retrogressive state and stagelist.

[0043]FIG. 30 is a flowchart of a behavior pattern transformingprocessing procedure.

[0044]FIG. 31 is a flowchart of a retrogressive processing procedure.

[0045]FIG. 32 is a conceptual diagram of other embodiments.

[0046]FIG. 33 is a block diagram instrumental in describing theprocessing of the controller in a fourth embodiment.

[0047]FIG. 34 is a conceptual diagram instrumental in describing aplurality of behavior and motion models provided for each behaviorpattern.

[0048]FIG. 35 is a conceptual diagram of a learning speed table.

BEST MODE OF CARRYING OUT THE INVENTION

[0049] A few preferred embodiments of the present invention areelucidated hereunder:

[0050] (1) First Embodiment

[0051] (1-1) Structure of a Pet Robot in a First Mode of Carrying outthe Present Invention

[0052] The ┌1┘ in FIG. 1 is a pet robot in whole in a first embodimentwherein a leg unit 3A˜3D is connected to a body unit 2, one each at theleft and right part of the front and rear sides, and a head unit 4 andtail unit 5 at the front and rear ends respectively.

[0053] In this case, as shown in FIG. 2, a body unit 2 houses acontroller 10 controlling the overall operation of the pet robot 1, abattery 11 as power source for the pet robot 1, and an internal sensorunit 15 made up of a battery sensor 12, thermal sensor 13 and anacceleration sensor 14.

[0054] Also, placed at the designated positions on the head unit 4 arean external sensor unit 19 made up of a microphone 14 for the ‘ear’ ofthe pet robot 1, CCD camera 17 (Charge Coupled Device) for the ‘eye’ anda touch sensor 18, and a speaker for the ‘mouth’ respectively.

[0055] An actuator 21 ₁˜21 _(n) is placed at the joint of each leg unit3A˜3D, at the linkage point of each leg unit 3A˜3D and the body unit 2,at the linkage point of the head unit 4 and body unit 2 as well as atthe linkage point of the tail unit 5 and body unit 2.

[0056] The microphone of the external sensor unit 19 collects commandsounds given in terms of a scale through a sound commander (not shown infigure), such as ‘Walk’, ‘Lie down’, or ‘Chase the ball’, a resultantvoice signal S1A of which is fed to the controller 10. The CCD cameratakes a picture of the surroundings, an image signal S1B obtained fromwhich is sent to the controller 10.

[0057] The touch sensor 18 located at the upper part of the head unit 4,as is apparent from FIG. 1, detects a pressure received, which iscreated as a result of a physical influence such as ‘Stroke’ or ‘Pat’exerted by the user, and the detected result is fed to the controller asa pressure detection signal S1C.

[0058] The battery sensor 12 of the internal sensor unit 15 detects theresidual energy of the battery 11, of which result is sent to thecontroller 10 as a battery residue detection signal S2A. The thermalsensor 13 detects a temperature inside the pet robot 1 whose result issent to the controller 10 as a temperature detection signal S2B. Theacceleration sensor 14 detects acceleration in the direction of 3 axes(X, Y and Z) whose result is transferred to the controller 10 as anacceleration detection signal S2C. The controller 10 judges external andinternal states and the existence of a command or influence from theuser based on the voice signal S1A given from the external sensor 19,image sensor S1B and pressure detection signal S1C (these two are puttogether and called external information signal S1 hereinafter), thebattery residue detection signal S2A supplied by the internal sensorunit 15, temperature detection signal S2 B and acceleration detectionsignal S2C, etc. (they are put together and called internal informationsignal S2 hereinafter).

[0059] The controller 10 determines the next behavior based on theforegoing judgment result and a control program stored beforehand in thememory 10A, and drives the actuator 21 ₁˜21 _(n) based on the resultobtained so as to let the pet robot 1 perform behaviors and motions suchas swinging the head unit 4 up and down, left and right, moving the tail5A of the tail unit 5 and walking by driving the leg unit 3A˜3D.

[0060] At the same time, the controller 10 generates a voice signal S3as required, which is fed to the speaker 20 to output a voice outsidebased on the voice signal S3 and blink an LED (Light Emitting Diode, notshown in figure) placed at the position where the eyes of the pet robot1 are supposed to be.

[0061] In this way the pet robot 1 is designed to be capable of actingautonomously responding to a state inside and outside of it and commandsand influence from the user. In addition to the foregoing operations,the pet robot 1 is also designed to transform its behaviors and motionsas if it were ‘growing’ like a real animal, according to the history ofinput operations such as influence and sound commands exerted by theuser and the history of its own behaviors and motions.

[0062] That is to say, the pet robot 1 comprises, as understood fromFIG. 3, the four stages of ‘Baby’, ‘Child’, ‘Young’ and ‘Adult’ as agrowth process. Stored beforehand in the memory 10A of the controller 10are behavior and motion models composed of various control parametersand control programs, which form the bases for behaviors and motionsrelating to four items: ‘walking state’, ‘motion’, ‘behavior’ and‘(barking) sound’ for each ‘growth stage’.

[0063] The controller 10 then controls each actuator 21 ₁˜21 _(n) andvoice output such that, in accordance with the behavior and motionmodels of ‘Baby’ in the initial stage, as to the ‘walking state’ forexample, the pet robot 1 toddles with narrower walking steps, shorterwalking periods and lower leg movements, and as to the ‘motions’, thepet robot 1 conducts just such ‘monotonous’ actions as ‘walk’, ‘stand’,and ‘lie down’, and as to the behaviors, the pet robot 1 performs just‘monotonous’ behaviors, repeating similar ones, and as to the ‘sounds’,the pet robot 1 barks in a small and short voice by lowering theamplification of the voice signal S3.

[0064] Simultaneously the controller 10 watches for and counts thenumber of occurrences in respect to a plurality of elements (referred toas ‘growth elements’ hereinafter) contributing to the predetermined‘growth’, such as command inputs by means of sound commands, reinforcedlearning made up of command inputs corresponding to ‘stroke’ and ‘pat’entered with the use of the sound commands and the number of successesin conducting predetermined behaviors and motions, sensor inputs notcorresponding to ‘stroke’ and ‘pat’ entered through the touch sensor 18,predetermined behaviors and motions such as ‘play with the ball, andother elements.

[0065] The controller 10 transforms behavior and motion models to beused, from the behavior and motions models for ‘Child’ into a higher setof behavior and motion models for ‘Young’ based on the accumulated sumof frequencies of those growth elements when the total value ofaccumulated sum of frequencies of each growth element (this is referredto as ‘integrated experience value of the growth elements’ hereinafter)exceeds a preset threshold.

[0066] The controller 10 then controls each actuator 21 ₁˜21 _(n) and avoice output from the speaker 20 in accordance with the behavior andmotion models for ‘Child’, such that the pet robot 1, as to the ‘walkingstate’, walks a bit more firmly with each actuator 21 ₁˜21 _(n) rotatedfaster, longer periods of time and the legs raised higher, as to‘motion’, moves with ‘a bit more enhanced and intricate’ movements byincreasing the number of motions, as to ‘behavior’, behaves with ‘a bitof objectiveness’ by determining the next behavior referring to theprevious one, and as to ‘sound’, barks in ‘a bit longer and loudervoice’ by prolonging and amplifying the length of a voice signal.

[0067] Furthermore, the controller 10 transforms, in a similar manner,the behavior and motion models in order into behavior and motion modelsof a higher growth level for ‘Young’ or ‘Adult’ whenever the integratedexperience value of the growth elements exceeds each threshold presetfor ‘Young’ or ‘Adult’. Simultaneously the rotation speed of eachactuator 21 ₁˜21 _(n) is varied according to the corresponding behaviorand motion models so as to have the walking periods prolonged, the legsraised higher, or to increase gradually the length and amplification ofthe voice signal S3 fed to the speaker 20. That is, the number ofrotation of the actuator 21 ₁˜21 _(n) is varied for each behavior ormotion.

[0068] Consequently, as a growth stage rises (from ‘Baby’ to ‘Child’,from ‘Child’ to ‘Young’, from ‘Young’ to ‘Adult’), the ‘walking state’of the pet robot 1 transforms from ‘toddle’ to ‘walk more firmly’, the‘motion’ from ‘monotonous’ to ‘enhanced, intricate’, and ‘behavior’ from‘monotonous’ to ‘behave with objectiveness’, and the ‘sound’ varies from‘small and short’ to ‘longer and louder’ by stages.

[0069] In this manner the pet robot 1 is designed to grow in fourstages, namely ‘Baby’, ‘Child’, ‘Young’ and ‘Adult’, according to theinputs from the outside and the history of the behaviors and motions ofits own.

[0070] In the case of this embodiment, a plurality of behavior andmotion models are, as is apparent from FIG. 3, prepared for each ‘growthstage’ of ‘Baby’, ‘Child’, ‘Young’ and ‘Adult’.

[0071] In practice, for example, as behavior and motion models for‘Child’, the behavior and motion models (Child 1) are prepared based onwhich the behaviors and motions of particular behavior patterns areconducted to represent quick but rough ‘wild’ movements, and another setof behavior and motion models (Child 2) based on which the behaviors andmotions of particular behavior patterns to represent smooth and slow‘calm’ movements.

[0072] For the ‘Young’ behavior and motion models, three sets ofbehavior and motion models are prepared; Young 1: behaviors and motionmodels for conducting behaviors and motions of much quicker and rougher‘irritating’ movements compared to the ‘wild’ behavior patterns for‘Child’, Young 2: behavior and motion models for conducting behaviorsand motions of slower and smoother moving ‘normal’ behavior patterns,Young 3: behavior and motion models for conducting behaviors and motionsof much slower moving ‘calm’ behavior patterns with a less amount ofactive movements.

[0073] Provided furthermore as behavior and motion models for ‘Adult’are; Adult 1: behavior and motion models (Adult 1) for conductingbehaviors and motions of ‘aggressive’ behavior patterns with a qualityof rougher and quicker movements, performing motions not conforming tothe commands from the user, Adult 2: behavior and motion models forconducting ‘a bit wilder’ behavior patterns with a quality of smootherand slower movements, performing motions conforming to the commands fromthe user, Adult 3: the behavior and motion models for conductingbehaviors and motions of ‘a bit calmer’ behavior patterns with a qualityof smoother and slower movements with a small quantity of motions,always performing motions conforming to the commands from the user, andAdult 4: behavior and motion models for conducting behavior and motionsof ‘calm’ behavior patterns with a quality of much slower movements witha less amount of motions, always performing motions conforming to thecommands from the user.

[0074] The controller 10 is designed such that in raising a ‘growthstage’, one of the behavior and motion models is selected from among thebehavior and motion models in the next ‘growth stage’ based on theaccumulated sum of frequencies of each growth element, and that theselected behavior and motion model is used for the next motion in placeof the behavior and motion model previously used. In this case, intransiting to the next ‘growth stage’ after the ‘Child’, the behaviorand motion models of the current ‘growth stage’ can transit only to thepredetermined behavior and motion models of the next ‘growth stage’,i.e. just among the behavior and motion models connected with the arrowsas shown in FIG. 3. Accordingly, if the behavior and motion model ‘Child1’ for conducting ‘wild’ behaviors and motions is selected for ‘Child’,for example, the pet robot 1 is not allowed to transit to the behaviorand motion model ‘Young 3’ for ‘Young’, which performs ‘calm’ behaviorsand motions.

[0075] In this manner the pet robot 1 is designed such that its‘behavior patterns’ transform as it grows according to the input historyof influence and commands from the user and the history of behaviors andmotions of its own.

[0076] (1-2) Processing of Controller 10

[0077] Concrete processing of the controller 10 of the pet robot 1 isdescribed hereunder.

[0078] The contents of the processing of the controller 10 arefunctionally divided as follows, as shown in FIG. 4:

[0079] state recognition mechanism unit 30 for recognizing external andinternal circumstances

[0080] feeling/instinct modeling unit 31 for determining the state offeeling and instinct based on the results recognized by the staterecognition mechanism unit 30

[0081] behavior determining mechanism unit 32 for determining the nextbehavior or motion based on the result recognized by the staterecognition mechanism unit 30 and the output by the feeling/instinctmodeling unit 31

[0082] posture transition mechanism unit 33 for making a plan for aseries of motions based on which the pet robot 1 performs behaviors andmotions determined by the behavior determining mechanism unit 32

[0083] device control mechanism unit 34 for controlling the actuator 21₁˜21 _(n) based on the plan made by the posture transition mechanismunit 33, and

[0084] growth control mechanism unit 35 for controlling ‘growth’.

[0085] Elucidation is given on the state recognition mechanism unit 30,feeling/instinct modeling unit 31, behavior determining mechanism unit32, posture transition mechanism unit 33, device control mechanism unit34, and growth control mechanism unit 35 following.

[0086] (1-2-1) Configuration of State Recognition Mechanism Unit 30

[0087] The state recognition mechanism unit 30 recognizes a particularstate based on the external information signal S1 and internalinformation signal S2, the result of which is conveyed to thefeeling/instinct modeling unit 31 and behavior determining mechanismunit 32.

[0088] In practice the state recognition mechanism unit 30 constantlywatches for a voice signal S1A given from the microphone 16 of theexternal sensor unit 19 and recognizes that a command is given when aspectrum of the same scale is detected as the command sound outputtedfrom the sound commander, according to a command such as ‘walk’, ‘liedown’ or ‘chase the ball’ given as the spectrum of the voice signal S1A,the result of which is conveyed to the feeling/instinct modeling unit 31and behavior determining mechanism unit 32.

[0089] The state recognition mechanism unit 30 also constantly watchesfor the image signal S1B given from the CCD camera 17 (FIG. 2) andrecognizes, for example, a state ‘there is a ball’ or ‘there is a wall’if it detects a ‘red, round ball’ or a ‘plane perpendicular to andhigher than the ground’ within the image based on the image signal S1B,the result of which is conveyed to the feeling/instinct modeling unit 31and behavior determining mechanism unit 32.

[0090] Furthermore the state recognition mechanism unit 30 constantlywatches for a pressure detection signal S1C given from the touch sensor18 (FIG. 2) and recognizes a state ‘patted (scolded)’ when a pressure isdetected which is larger than a given threshold based-on the pressuredetection signal S1C for a short period of time (e.g. less than 2seconds), and a state ‘stroked (praised) when a pressure is detectedwhich is less than a given threshold for a long period of time (e.g.longer than 2 seconds), the result of which is conveyed to thefeeling/instinct modeling unit 31 and behavior determining mechanismunit 32.

[0091] The state recognition mechanism unit 30 also constantly watchesfor an acceleration detection signal S2C given from the accelerationsensor 14 (FIG. 2) of the internal sensor unit 15 and recognizes a state‘received a big impact when an acceleration is detected which is largerthan e.g. that of a preset given level based on the acceleration signalS2C, while it recognizes a state ‘fell (from the table, etc.)’ when anacceleration is detected which is about the gravity acceleration largerthan the former, the result of which is conveyed to the feeling/instinctmodeling unit 31 and behavior determining mechanism unit 32.

[0092] Also, the state recognition mechanism unit 30 constantly watchesfor a temperature detection signal S2B given from the temperature sensor13 (FIG. 2) and recognizes a state ‘the internal temperature has risenwhen a temperature is detected which is larger than a given value basedon the temperature detection signal S2B, the result of which is conveyedto the feeling/instinct modeling unit 31 and behavior determiningmechanism unit 32.

[0093] (1-2-2) Processing of Feeling/Instinct Modeling Unit 31

[0094] The feeling/instinct modeling unit 31 comprises, as shown in FIG.5, a basic feeling/motion group 40 consisting of feeling modelscorresponding to each of six (6) feelings or motions: ‘joy’, ‘grief’,‘surprise’, ‘fear’, ‘dislike’, and ‘anger, and a basic desire group 41consisting of desire units 41A˜41D provided as desire modelscorresponding to each of four (4) desires: ‘appetite, ‘desire foraffection’, ‘inquisitiveness’, and ‘desire to move’, and intensityincrease/decrease functions 42A˜42H provided corresponding to each ofthe feeling/motion units 40A 40F and desire units 41A˜41D.

[0095] Each of the feeling/motion units 40A˜40F indicates the intensityof a corresponding feeling or motion in terms of e.g. levels from 0 to100, which constantly varies based on the intensity informationS11A˜S11F given from the intensity increase/decrease functions 42A˜42H,corresponding to the current intensity.

[0096] Each of the desire units 41A˜41D indicates, as the feeling/motionunit 40A˜40F does, the intensity of the corresponding desire in terms oflevels from 0 to 100, which constantly varies based on the intensityinformation S12G˜S12F given from intensity increase/decrease function42G˜42K, corresponding to the current intensity.

[0097] The feeling/instinct modeling unit 31 determines a state offeeling by combining the intensities of the feeling/motion units 40A˜40Fand a state of instinct by combining the intensities of the desire units41A˜41D. The state of feeling and instinct is outputted asfeeling/instinct state information S12 at the behavior determiningmechanism unit 32.

[0098] The intensity increase/decrease functions 42A˜42G are thefunctions for generating and outputting intensity information S11A S11Gfor increasing or decreasing the intensity of each of the feeling/motionunits 40A˜40F and desire units 41A˜41D according to the presetparameters as described above, based on the state recognitioninformation S10 given from the state recognition mechanism unit 31 andbehavior information S13 indicating the contents of the present or pastbehaviors of the pet robot 1 itself.

[0099] Thus the characteristics such as ‘irritation’ or ‘calmness’ areput into the pet robot 1 by setting different values to the parametersof the intensity increase/decrease functions 42A˜42G for each behaviorand motion model (Baby 1, Child 1, Child 2, Young 1˜Young 3, Adult1˜Adult 4).

[0100] (1-2-3) Processing of Behavior Determining Mechanism Unit 32

[0101] The behavior determining mechanism unit 32 has a plurality ofbehavior models inside the memory 10A, each corresponding to each ofbehavior and motion model (Baby 1, Child 1, Child 2, Young 1, Young1˜Young 3, Adult 1˜Adult 4).

[0102] And, the behavior determining mechanism unit 32 determines thenext behavior or motion based on the state recognition information 10given from the state recognition mechanism unit 30, the intensity ofeach of the feeling/motion unit 40A˜40F and desire unit 41A˜41D of thefeeling/instinct modeling unit 31, and the corresponding behavior model,the result of which is outputted as determined behavior information 14at the posture transition mechanism unit 33 and growth control mechanismunit 35.

[0103] In this instance the behavior determining mechanism unit 32 usesan algorithm called probability automaton, as a means for determiningthe next behavior or motion, in determining with probability, to whichnode ND_(A0)˜ND_(An) a particular node (state) ND_(A0) as shown in FIG.6, should transit including itself based on the transition probabilityP_(o)˜P_(n), each set to an arc AR_(A0)˜AR_(An) connecting the nodesND_(A0)˜ND_(An).

[0104] More concretely the memory 10A stores a state transition table 50as shown in FIG. 7, as behavior models, for each node ND_(A0)˜ND_(An)and the behavior determining mechanism unit 32 determines the nextbehavior or motion based on this state transition table 50.

[0105] In the state transition table 50 the input events (recognitionresults) are enumerated on the ‘Input Event’ line with priority, whichare transition conditions in the node ND_(A0)˜ND_(An), and furtherconditions regarding the transition conditions are described on thecolumns corresponding to the ‘Data Name’ and ‘Data Range’ lines.

[0106] Accordingly, in the node ND₁₀₀ defined on the state transitiontable 50 in FIG. 7, given a recognition result ‘detected a ball (BALL)’,this recognition itself and an event given at the same time that the‘size’ of the ball given is within the range of ‘from 0 to 1000(0,1000)’ is the condition for the current node ND₁₀₀ to transit toanother one. Likewise, given a recognition result ‘detected an obstacle(OBSTACLE), this recognition result itself and an event given at thesame time that the ‘distance (DISTANCE)’ to the obstacle is within therange of ‘from 0 to 100 (0, 100) is the condition for the current nodeND₁₀₀ to transit to another node.

[0107] Also, if there is no recognition result inputted into the nodeND₁₀₀, it can transit to another node if, of the intensities of thefeeling/motion units 40A˜40F and desire units 41A˜41D of thefeeling/instinct modeling unit 31 referred periodically by the behaviordetermining mechanism unit 32, the intensity of the feeling/motion unit40A˜40F of any of ‘joy (JOY), ‘surprise (SURPRISE) or ‘sadness(SADNESS)’ is ‘within the range of from 50 to 100 (50, 100).

[0108] As to the state transition table 50, the names of nodes to whichthe node is allowed to transit are enumerated on the row of ‘Nodes towhich the current node can transit’ in the column of the ‘transitionprobability to other nodes’ as well as the transition probability on theline of the ‘Output Actions’ in the column of the ‘TransitionProbability to other Nodes’, which lets the current node transit to anyof the other nodes, when all the conditions listed on each line of the‘Name of Input Events’, ‘Data Value’ and ‘Range of Data’ are satisfied.The sum of the transition probabilities on each line in the column ofthe ‘Transition Probability to Other Nodes’ is 100 ┌%┘.

[0109] Accordingly, in this case, given the recognition results, forexample, that ‘the ball has been detected (BALL) and that the ‘SIZE’ ofthe ball is within the range of from 0 to 1000 (0,1000), the node cantransit from the NODE₁₀₀ to the ‘NODE₁₂₀’ (node 120) with a probabilityof ‘30 ┌%┘’, and the behaviors and motions of the ‘ACTION 1’ areoutputted at this time.

[0110] Each of the behavior models are made up of a number of nodes ofthe same nodes connected, described in the state transition table 50.

[0111] In this way the behavior determining mechanism unit 32 determineswith probability the next behavior or motion (a behavior or motiondescribed on the line ┌Output Behaviors┘ using the state transitiontable 50 of the corresponding node of the suitable behavior modelsstored in the memory 10A at such a time as when state recognitioninformation 10 is given from the state recognition mechanism unit 30 orwhen a given period of time has elapsed since the last behavior wasdiscovered, the result of which is outputted as behavior commandinformation S14 at the posture transition mechanism 33 and growthcontrol mechanism 35.

[0112] (1-2-4) Processing of posture transition mechanism unit 33 Givenbehavior command information 14 from the behavior determining mechanismunit 32, the posture transition mechanism unit 33 makes a plan for aseries of behaviors for the pet robot 1 to perform behaviors or motionsbased on the determined behavior information 14 and outputs behaviorcommand information S15 based on the behavior plan at the controlmechanism unit 34.

[0113] In this case the posture transition mechanism unit 33 uses, as ameans of making an action plan, a directed graph representing a posturethe pet robot 1 may take, as shown in FIG. 8, as node ND_(B0)˜NDB2, ofwhich nodes that can transit to one another are connected by a directedarc AR_(B0)˜AR_(B2) expressing behaviors, and representing a behaviorterminating at one node among the nodes ND_(B0)˜ND_(B2) as self-actingarc AR_(C0)˜AR_(C2).

[0114] For this purpose the memory 10A stores data of files in the formof data base containing a starting posture and an ending posture of allthe behaviors (these files are called ‘network definition files’hereinafter) the pet robot 1 can take, which is the source of thedirected graph, and the posture transition mechanism unit 33 creates adirected graph 60˜63(as shown in FIG. 9˜12) for each of the whole body,head, legs and tail based on the network definition files.

[0115] As is apparent from FIG. 9˜12, the postures the pet robot 1 maytake are largely divided into the four (4) groups: ┌stand (o Standing)┘,┌sit (o Sitting)┘, ┌Lie down (o Sleeping)┘, and ┌station (o Station)┘which is the posture taken on the charger cradle (not shown in Fig.) tohave the battery 11 (FIG. 2) charged. Each group of postures has basepostures (marked ⊚) used in common for all the ‘growth stages’ and oneor a plurality of normal postures (marked o) for ‘Baby’, ‘Child’,‘Young’, and ‘Adult’.

[0116] For example, the portions enclosed by the broken lines in FIG.9˜12 represent normal postures for ‘Baby’, and, as can be known fromFIG. 9, there are prepared ┌o Sleeping b (baby)┘, ┌o Sleeping b2┘˜┌oSleeping b5┘ as normal postures of ┌lie down┘ for ‘Child’, and ┌oSitting b┘ and ┌o Sitting b2┘ as normal postures of ┌sit┘.

[0117] And, the posture transition mechanism unit 33, given behaviorcommand information S14 from the behavior determining mechanism unit 32,such as ‘Stand up’, ‘Walk’, ‘Offer a hand’, ‘Swing the head’ or ‘Wag thetail’, the posture transition mechanism unit 33, with the use of thecorresponding directed graph 60˜63 and following the direction of adirected arc, searches for the route to a node to which a posturedesignated by the current node corresponds, or a directed arc orself-acting arc to which an appointed behavior corresponds. Thenbehavior commands are outputted as behavior command information S15 atthe device control mechanism unit 34, based on which the pet robot 1performs behaviors in order, corresponding to each directed arc on theroute obtained.

[0118] For example, if the pet robot 1 is in the state of ┌o Sitting b┘and when a behavior command to perform a behavior present at the node ┌oSleeping b4┘ (behavior corresponding to a self-acting arc_(a1)) is givento the posture transition mechanism unit 33 from the behaviordetermining mechanism unit 32, the posture transition mechanism unit 33searches for a route to the node ┌o Sleeping b4┘ from the node ┌oSitting b┘ on the directed graph 60 for the whole body, and then outputsbehavior commands in order as behavior command information 15 at thecontrol mechanism unit 34 to transit the current posture from the node┌Sitting b┘ to the node ┌o Sleeping b5┘, from the node ┌o Sleeping b5┘to the node ┌o Sleeping b3┘, and from the node ┌o Sleeping b3┘ to thenode ┌o Sleeping b4┘, and finally a behavior command as behavior commandinformation S15 at the control mechanism unit 34 to return to the node┌o Sleeping b4┘ through the self-acting arcs oriented to a behaviordesignated by the node ┌o Sleeping b4┘.

[0119] There may be a case at this stage that two nodes to which thecurrent node may transit are connected with a plurality of directed arcsto transform a behavior (‘wild behavior, ‘calm’ behavior, etc.)according to the ┌growth stage┘ and ┌behavior patterns┘ of the pet robot1. In this case the posture transition mechanism unit 33 selects adirected arc as a route corresponding to the ┌growth stage┘ and┌behavior patterns┘ where and which the pet robot 1 is and has acquiredat and by that time, under the control of the growth control mechanismunit 35 (to be described later).

[0120] Similarly there may be a case that a plurality of self-actingarcs are provided to return to the corresponding node from a certainnode to transform a motion according to a ┌growth stage┘ and ┌behaviorpattern┘. In this case, too, the posture transition mechanism unit 33selects a directed arc as a route corresponding to the ┌growth stage┘and ┌behavior patterns┘ where and which the pet robot 1 is and hasacquired at and by that time.

[0121] During a posture transition described above the period of timefor it to remain on the way is almost ‘0’, so that the transition may bedone via a node used for another ┌growth stage┘ during the posturetransition.

[0122] Consequently the posture transition mechanism unit 33 searchesfor the shortest route to the next node, or directed arc or self-actingarc from the current node, regardless of the current ┌growth stage┘.

[0123] In a case that a behavior command is given to the head unit, legunits or tail unit, the posture transition mechanism unit 33 returns aposture of the pet robot 1 to any of the base postures (marked ⊚) basedon the directed graph 60 for the whole body, and then outputs behaviorcommand information S15 to let the posture of the head unit, leg units,or tail units transit, using the directed graph 61˜63 corresponding tothe head unit, let units, or tail unit.

[0124] (1-2-5) Processing of Device Control Mechanism Unit 34

[0125] The control mechanism unit 34 generates a control signal S16based on behavior command information S15 given from the posturetransition mechanism unit 33, and lets the pet robot 1 perform anappointed behavior and motion by driving each actuator 21 ₁˜21 _(n)based on the control signal S16.

[0126] (1-2-6) Processing of Growth Control Mechanism Unit 35

[0127] The growth control mechanism unit 35 is supplied with variousstates recognized as a state recognition signal S20 based on theexternal information signal S2 and internal information signal S1 givenfrom the state recognition mechanism unit 30. As described above, thevarious states includes inputs entered through the touch sensor 18, notstrong enough to be identified with, for example, ‘stroked’ or ‘patted’,in addition to particular states conveyed to the feeling/instinctmodeling unit 31 and behavior determining mechanism unit 32.

[0128] The growth control mechanism unit 35 also has inside the memory10A the list 70A (referred to as the first growth element list) as shownin FIG. 13A containing the foregoing growth elements which should bereferenced in raising a ┌growth stage┘ of the various states based onthe state recognition information S20 given from the state recognitionmechanism unit 30, and the counter table 70B (referred to as the firstgrowth element counter table) as shown in 13B to count the accumulatednumber of frequencies of these growth elements.

[0129] The growth control mechanism unit 35, given the state recognitioninformation 20 from the state recognition mechanism unit 30, judgeswhether or not a state to be obtained based on the state recognitioninformation 20 referring to the first growth element list 70A, and ifthis state is found to be a growth element, the corresponding countvalue (experience value) in the growth counter table 70B is increased by‘1’.

[0130] Also, the growth control mechanism unit 35 has inside the memory10A the list 71A (referred to as the second growth element list) asshown in FIG. 14A, containing the foregoing growth elements which shouldbe referenced in raising a ┌growth stage┘ of the various behaviors to beobtained based on the behavior command information S14 given from thebehavior determining mechanism unit 32, and the counter table 71B(referred to as the second growth element counter table) as shown inFIG. 14B to count the accumulated number of frequencies of these growthelements.

[0131] The growth control mechanism unit 35, given the behavior commandinformation S14 from the behavior determining mechanism unit 32, judgeswhether or not a behavior or motion to be obtained is a growth elementbased on the behavior command information S14 referring to the secondgrowth element list 71A, and if this behavior is found to be a growthelement, the corresponding count value (experience value) in the secondgrowth counter table 71B is increased by ‘1’.

[0132] Furthermore, when the counter values in the first and secondgrowth element counter tables 70B, 71B are increased as described above,the growth control mechanism unit 35 increases the count value by ‘1’ ofthe counter (referred to as ‘integrated growth experience value counter’hereinafter) to judge whether or not a ┌growth stage┘ preparedseparately from the first and second counter tables 70B, 71B and judgeswhether or not the counter value of the integrated growth experiencevalue counter has reached the counter value preset as a terminationcondition for the current ‘growth stage’.

[0133] If the counter value of the integrated growth experience valuecounter has reached the counter value preset as a termination conditionfor the current ‘growth stage’, the growth control mechanism unit 35determines a behavior or motion within the next ‘growth stage to whichthe current behavior or motion should be transformed based on each countvalue in the first and second growth element counter tables 70B, 71B,the results of which are conveyed to the feeling/instinct modeling unit31, behavior determining mechanism unit 32 and posture transitionmechanism unit 33. However, if the pet robot 1 is in the initial stage,a command is given to the feeling/instinct modeling unit 31, behaviordetermining mechanism unit 32 and posture transition mechanism unit 33to select the behavior and motion models for ‘Child’.

[0134] As a result, the feeling/instinct modeling unit 31 changes, basedon the command transforming information S22, the parameter of each ofthe intensity increase/decrease function 42A˜42G described in FIG. 5 tothe value of a behavior or motion designated. The behavior determiningmechanism unit 32 transforms a behavior model to be used to that of abehavior and motion model designated based on the command transforminginformation S22. The posture transition mechanism unit 33 changes thesetting, so that the directed arc and self-acting arc of a behavior andmotion model designated is selected according to the commandtransforming information S22 in such a case that any directed arc orself-acting arc must be selected from among the directed arcs andself-acting arcs corresponding to a plurality of subsequent behavior andmotion models.

[0135] As can be known from the foregoing, the behavior and motion modelcomprises the parameter value of each of the intensity increase/decreasefunction 42A˜42B in the feeling/instinct modeling unit 31 correspondingto the ┌behavior pattern┘ in a particular ┌growth state┘, a behaviormodel in the behavior determining mechanism unit 32, and a directed arcor self-acting arc in the posture transition mechanism unit 33.

[0136] In this manner the controller lets the pet robot 1 generatebehaviors to be capable of acting autonomously, raising the ‘growthstage’ as required.

[0137] (1-3) Growth Control Processing Procedure RT1

[0138] The growth control mechanism unit 35 controls the ‘growth stages’of the pet robot 1 according to a growth control processing procedureRT1 as shown in FIG. 15.

[0139] The growth control mechanism unit 35 starts executing this growthcontrol processing procedure RT1 at the step SP1 after the power isturned on for the first time, and judges at the subsequent step SP2whether or not state recognition information S10 is given from the staterecognition mechanism unit 30.

[0140] If a negative result is obtained at the step SP2, the growthcontrol mechanism unit 35 proceeds to the step SP3 and judges whether ornot behavior determining information S14 is given from the behaviordetermining mechanism unit 32 (FIG. 4). The growth control mechanismunit 35 returns to the step SP2 if a negative result is obtained at thestep SP3 and repeats an SP2-SP3-SP2 loop operation until an affirmativeresult is obtained at the step SP2 or step SP3.

[0141] When an affirmative result is obtained at the step SP2 in duecourse, the growth control mechanism unit 35 proceeds to the step SP4and judges whether or not a state to be obtained is a growth elementaccording to the state recognition information S10 given from the staterecognition mechanism unit 30.

[0142] The growth control mechanism unit 35 returns to the step SP2 if anegative result is obtained at the step SP4, while if an affirmativeresult is obtained, the growth control mechanism unit 35 proceeds to thestep SP5, and increases by ‘1’ a count value corresponding to the firstgrowth element list 70A (FIG. 13A) and a count value of the integratedexperience value counter.

[0143] Subsequently the growth control mechanism unit 35 proceeds to thestep SP6 and judges whether or not the value of the integratedexperience value counter reaches the count value preset as a terminationcondition for the current ‘growth stage’.

[0144] The growth control mechanism unit 35 returns to the step SP2 if anegative result is obtained at this step SP6, while if an affirmativeresult is obtained, it proceeds to the step SP7 and determines abehavior and motion model in the subsequent ‘growth stage to which thecurrent behavior and motion model should transit, the result of which isconveyed to the feeling/instinct modeling unit 31, behavior determiningmechanism unit 32 and posture transition mechanism unit 33, and then thegrowth control mechanism unit 35 returns to the step SP2.

[0145] If an affirmative result is obtained at the step SP3, the growthcontrol mechanism unit 35 proceeds to the step 8 and judges whether ornot a behavior to be obtained is a growth element according to thebehavior determining information S13 given from the behavior determiningmechanism unit 32.

[0146] The growth control mechanism unit 35 returns to the step SP2 if anegative result is obtained at this step SP8, while if an affirmativeresult is obtained, the growth control mechanism unit 35 proceeds to thestep SP5 and increases by ‘1’ the count value corresponding to thesecond growth element list 71A (FIG. 14A) and the count value of theintegrated experience value counter respectively, and proceeds to thestep SP6 to execute a process similar to the foregoing.

[0147] (1-4) Operations and Effects in the Present Embodiment

[0148] Configured as described above, the pet robot 1 grows gradually tobehave and act like an adult as the user exerts such an action as ‘pat’or ‘stroke’ on the pet robot 1, or gives it a command with using thesound commander, or the pet robot itself 1 plays with the ball.

[0149] Consequently the pet robot 1 may give a greater sense affinityand satisfaction to the user, exceeding the concept that the robot justwalks.

[0150] Also, as the pet robot 1 ‘grows’, its ‘behavior patterns arevaried according to the input history from the user and the history ofbehaviors and motions of its own, hence it may give a greater sense ofamusement (entertainingness) to the user.

[0151] With the foregoing configuration wherein the behaviors andmotions of the pet robot 1 are transformed as if it grew based on theactions and commands exerted on it by the user and the behaviors andmotions of the pet robot itself, so that the pet robot 1 may give agreater sense of affinity and satisfaction to the user. Thus a pet robotmay be realized whose amusement quality is substantially enhanced forthe user.

[0152] (1-5) Other Modes of Carrying out the Present Invention

[0153] In the mode of the foregoing first embodiment, elucidation isgiven on the case wherein the present invention is applied to thefour-footed robot configured as shown in FIG. 1. However, the presentinvention is not limited to it, but applicable widely to robots of avariety of other structures.

[0154] In the mode of the foregoing first embodiment, elucidation isgiven on the case wherein the controller 10, actuators 21 ₁˜21 _(n)(FIG. 2) and speaker (FIG. 2), etc. are used as behavior and motiongeneration means to generate behaviors and motions based on the behaviorand motion models. However, the present invention is not limited to it,but a variety of other structures may be employed as behavior and motiongeneration means, depending upon the mode of a robot embodying thepresent invention.

[0155] Also, in the mode of the foregoing first embodiment, elucidationis given on the case wherein the growth control mechanism 35 of thecontroller 10 is used as behavior and/or motion transforming means totransform the behavior and/or motion models into behavior and/or motionmodels of a higher growth level at a given timing, based on at leaseeither of the input history from the outside and the history ofbehaviors and/or motions of its own. However, the present invention isnot limited to it, but a variety of other structures may be used asbehavior and/or motion transforming means, depending upon the mode of arobot embodying the present invention.

[0156] Furthermore, in the mode of the foregoing first embodiment,elucidation is given on the case wherein a robotic device 1 ‘grows! bystages. However, the present invention is not limited to it, but therobotic device may be so designed as to ‘grow’ with no stages bydetecting a state of growth elements and by varying the values of thecontrol parameters in order every time a behavior or motion of thegrowth elements is performed.

[0157] Furthermore, in the mode of the foregoing first embodiment,elucidation is given on the case wherein the robotic device grows’ byfour (4) stages: ‘Baby’, ‘Child’, ‘Young’, and ‘Adult’. However, thepresent invention is not limited to it, but the number of ‘growthstages’ may be set to other numbers than the number four (4).

[0158] In this case, similarly to the growth stage model shown in FIG.16, for example, when the transition enable conditions are satisfied ina certain cell 72, the robotic device 1 may be designed to ‘grow’ insuch a way that it is allowed to transit to a cell 72 of the cells 72adjacent to it, which is higher than its own growth level.

[0159] Furthermore, in the mode of the foregoing first embodiment,elucidation is given on the case wherein the history of contact inputsthrough the touch sensor 18 (FIG. 2) and the history of photographs bymeans of the CCD camera 17 (FIG. 2) and command inputs with use of soundcommander, etc. are used as the input history from the outside. However,the present invention is not limited to it, but the user may use theinput history by other means in addition to the foregoing or other meansonly than the foregoing.

[0160] Furthermore, in the mode of the foregoing first embodiment,elucidation is given on the case wherein a plurality of behavior andmotion models are prepared for each ‘growth stage’ after ‘Baby’.However, the present invention is not limited to it, but the onlybehavior and motion model may be prepared for each stage.

[0161] Furthermore, in the mode of the foregoing first embodiment,elucidation is given on the case wherein the four items of ‘walkingstate, ‘motion’, ‘behavior’, and ‘sound’ are designated as variables tovary along with ‘growing’. However, the present invention is not limitedto it, but other items than the foregoing may be used as variable to goalong with ‘growing’.

[0162] Furthermore, in the mode of the foregoing first embodiment,elucidation is given on the case wherein the pet robot 1 is so designedas to ‘grow’ based on the integrated experience value calculated basedon the integrated value of the accumulated sum of frequencies of eachgrowth element. However, the present invention is not limited to it, buta wide range of other calculation means may be employed to calculatedthe integrated experience value.

[0163] Furthermore, in the mode of the foregoing first embodiment,elucidation is given on the case wherein the ‘growth stage’ of the petrobot 1 is raised based on the input history from the outside and thehistory of its own behavior and motion. However, the present inventionis not limited to it, but only one of the input history and the historyof the behaviors and motions of its own may be used as an element toraise the ‘growth stage’. As well, other elements than the inputhistory, and the history of its own behavior and motion may be added tothe means to raise the growth stage.

[0164] In the case of adding other elements than the input history andthe history of its own behaviors and motions, the lapse of time andother things may be practically considered as the element. In using thelapse of time to raise the ‘growth stage’, for example, it may beadvisable to provide a growth element counter table for the lapse oftime, of which value is counted every time a given period of timeelapses, and to use the counted value of the growth element countertable, too, as a material to renew the integrated experience value.

[0165] Also, it may be advisable to let the pet robot 1 appraise thedegree of achievement of a certain behavior and motion of its own, forexample, a state of growth from a child able to walk on its feet to anadult capable of kicking the ball far away, with the use of a givenappraisal function, whose result may be used as an element to raise the‘growth stage’.

[0166] As shown in FIG. 17, for example, it is conceivable to let thepet robot 1 appraise the achievement degree ┌F┘ of an action ‘kick theball’ by the appraisal function f (d, θ) as defined by the growthcontrol mechanism unit 32 (FIG. 4) for an action ‘kick the ball’, andobtainable from the following expression:

F=a×d×cos(θ)

[0167] where, d is the distance to the ball 73 kicked with the center ofcoordinates of the pet robot 1 as the origin,

[0168] θ is the direction in which the ball is kicked, and,

[0169] a is a positive coefficient.

[0170] If the foregoing degree of achievement F exceeds the thresholdvalue preset for the current ‘growth stage’, it may be raised to thenext ‘growth stage’. The distance d to the ball 73 can be measured by adistance sensor provided separately, and the direction θ based on theoutput of an encoder (not shown in FIG. ) to measure the angle ofrotation of the output axis of the actuator 21 _(i) (_(i)is any of 1˜n)to rotate the head unit 4 in the direction of the roll. In this case,the farther the ball is kicked in the straight line, the larger thedegree of achievement F gets.

[0171] A wide variety of other behaviors and motions than an action‘kick the ball’ may be used as objects to be appraised, for example,‘walk’ (the speed of walking is appraised).

[0172] The degree of achievement of such behaviors and motions isconstantly calculated, and the next ‘growth stage’ or behavior patterns(Baby 1, Child 1, Child 2, Young 1˜Young 3, Adult 1˜Adult 4) may bedetermined based on the maximum value of the degree of achievementobtained in the process of (the pet robot 1) ‘growing’ according to theforegoing input history and the history of behaviors and motions of itsown.

[0173] (2) Second Mode of Carrying out the Present Invention

[0174] (2-1) Structure of a Pet Robot 80 in a Second Mode of CarryingOut the Present Invention

[0175] The ┌80┘ in the FIG. 1 shows a pet robot in whole in the secondembodiment, which is constructed in a way similar to the pet robot 1 inthe first embodiment, except that a different method is employed fortransforming behavior and motion models.

[0176] More concretely the five (5) ‘growth stages’ are provided for thepet robot 80 as shown in FIG. 18: ‘Tweety’, ‘Baby’, ‘Child’, ‘Young’,and ‘Adult’. With the pet robot 80 the contents the controller 81 (FIG.2) processes are divided into the units as shown in FIG. 19, wherein thesame reference numerals are assigned to the units corresponding to thosein FIG. 4, That is, the controller 81 is constructed in the same way asthe controller is in the first embodiment, except for a behaviordetermining mechanism unit 82 which has an enormous amount of statespace in which all the behavior patterns the pet robot 80 may realizeare stored.

[0177] The behavior determining mechanism unit 82 creates behavior andmotion models for each stage in such a way that with a portion of thestate space as a core in which the basic behaviors are generated for thepet robot 80, such as ‘walk’, ‘Sleep’ and ‘Stand’, etc., the only smallpart of the core is used for ‘Tweety’. After that, every time the petrobot 80 grows, it is allowed to transit to new partial state space tobe increased (partial state space in which new behaviors may take placeand a series of behavior patterns are generated) and separate thepartial state space which is used no longer (partial state space inwhich behaviors never take place and a series of behavior patterns arenot generated).

[0178] In the pet robot 80 the method in which the transitionprobability to the state space is varied as it grows is used as a meansto allow it to transmit to new partial state space to be increased andcut off unnecessary partial state space.

[0179] In FIG. 20, for example, assuming that an event ┌found a ball┘ isthe transition condition to transit from NODE_(A) to NODE_(B) and that aseries of evens, such as ┌approach and kick the ball┘ are the transitioncondition to transit from NODE_(B) to a series of node group 82, whenthe ball is found at the NODE_(A) a behavior pattern PA₁ ‘chase and kickit’ takes place with transition probability PA₁. However, in case thetransition probability P₁ is 0, the behavior pattern PA₁ never takesplace.

[0180] To let the pet robot 80 materialize such a behavior pattern PA₁,the transition probability P₁ is set to ‘0’ in the initial stage andvaried to a greater preset value than ‘0’ when it arrives at acorresponding ‘growth stage.

[0181] As opposed to it, to let the pet robot 80 forget the behaviorpattern PA₁ when a certain ‘growth stage’ is attained, the transitionprobability from NODE_(A) to NODE_(B) is varied to ‘0’ when that ‘growthstage’ is arrived at.

[0182] With the pet robot 80 the files 84A˜84D as shown in FIG. 21(referred to as ‘differential files’ hereinafter) are provided in eachbehavior and motion model, each corresponding to each ‘growth stage of‘Baby’, ‘Child’, ‘Young’, and ‘Adult’, as a concrete means to vary thetransition probability at the necessary places.

[0183] Stored in the differential files 84A˜84D are: the name of node(number) of the node (equivalent to NODE_(A) in FIG. 20) whosetransition probability should be varied to have the new behaviors as theforegoing take place when the ‘growth stage’ goes up, place of the nodein the state transition table 50 (FIG. 7) in which transitionprobability should be varied, and a varied transition probability in thecorresponding place.

[0184] The behavior determining mechanism unit 82 generates behaviorsusing the behavior and motion models for ‘Tweety in the initial stage,while when notification is given later from the growth control mechanismunit 35 that a ‘growth’ is attained as described before, the transitionprobability at each place appointed for each node defined in thecorresponding differential file 84A˜84D is varied to the predeterminedvalue based on the differential file 84A˜84D for the corresponding‘growth stage’.

[0185] In the cases shown in FIG. 7 and FIG. 21 by way of example, whenthe growth stage ‘Baby is attained, the transition probability locatedat the first column, on the first line in the area (portion below theline of ┌Output Behavior┘ and to the right of the ┌Range of Data┘column) where transition probabilities in the state transition table 50of the node NODE₁₀₀ are defined, is varied to ┌20┘ ┌%┘, and thetransition probability located at the nth column, on the first line inthe state transition table to ┌30┘ ┌%┘, and so on. At the same time thebehavior determining mechanism unit 82 varies the correspondingtransition probability of other node NODE₃₂₀, NODE₇₂₀ . . . defined inthe file 84A for ‘Baby’ as well.

[0186] As in this case, included among the transition probabilitieswhose values are to be varied, is a case wherein the transitionprobability up to a certain time is ┌0┘ (that is, transition to a nodeto be the origin of a series of behavior patterns is prohibited), or acase wherein the transition probability after being varied becomes ┌0┘(that is, transition to a node to be the origin of a series of behaviorpatterns is to be prohibited). As described in the foregoing, the casemay occur that the transition probability is varied to a given valuefrom ┌0┘, that the value of the transition probability after beingvaried becomes ┌0┘, that a series of the behavior patterns take place ata new ‘growth stage’, or that a series of the behavior patterns do nottake place.

[0187] Even in the case wherein necessary transition probabilities arevaried in this manner the value of each transition probability in eachof the differential file 84A˜84D is so designated that the sum of eachtransition probability included in the corresponding line in the statetransition table 50 after being varied becomes 100 ┌%┘.

[0188] Thus, as shown in FIG. 22 by way of example, the state space ofthe behavior and motion models for each ‘growth stage’ expands in orderas the pet robot 80 grows.

[0189] (2-2) Operations and Effects in this Mode of Carrying out thePresent Invention

[0190] Configured as in the foregoing, the pet robot 80 generates thebehavior and motion models for each ‘growth stage and behaves accordingto the behavior and motion models generated in such a manner that with aportion of the state space where the basic behaviors are conducted asthe core, out of the enormously expanded state space where all thebehavior patterns are stored, only a small portion including the core isused for ‘Tweety, and that a portion of the state space no longer to beused except for the core, is separated as the pet robot 80 grows, orthat transition to another portion of the state space to be added isallowed as the pet robot 80 grows.

[0191] With the pet robot 80, accordingly it is possible to represent‘growth’ more naturally because the state space of the behavior andmotion models in each ‘stage growth’ varies so continuously as toalleviate the discontinuity of the output behaviors before and after acertain ‘growth’. Also, with the pet robot 80, since the portion of thestate space where the basic behaviors are generated is used in commonfor all the ‘growth stages’, the result of learning of the basicbehaviors can be handed down to the next ‘growth stage’ in order.

[0192] Furthermore, with the pet robot 80, since a portion of the statespace in which the basis behavior is generated is used in common for allthe ‘growth stages’, the work of generating the behavior and motionmodels for each ‘growth stage’ can be done easily, and the amount ofdata of the behavior models are curtailed on the whole compared to thefirst mode wherein the behavior and motion models are preparedindividually for each ‘growth stage’.

[0193] Furthermore, in the pet robot 80, since the behavior and motionmodels for each ‘growth stage’ are generated by cutting off portions ofthe state space for a series of unnecessary behavior patterns and byallowing transition to a state space for a series of necessary behaviorpatterns as the pet robot 80 grows, each of a series of behaviorpatterns is modularized, enabling the behavior determining mechanismunit 82 to do generation work more easily as much.

[0194] Configured as in the foregoing, with the partial state space asthe core, in which the basic behaviors are conducted, out of theenormous state space where all the behavior patterns are stored, theonly portion including the core is used for ‘Tweety’, since portions ofthe state space not to be used any longer except for the core areseparated and the behavior and motion models for each ‘growth stage’ aregenerated by allowing transition to a portion of the state space to beadded anew, the state space of the behavior and motion models for each‘growth stage’ can be varied continuously, thereby curtailing thediscontinuity of output behaviors before and after a certain growth.Consequently the ‘growth’ is represented more naturally, and a pet robotcan be realized with a greatly increased entertaining quality.

[0195] (2-3) Other Modes of Carrying out the Present Invention

[0196] In the foregoing second embodiment elucidation is given on thecase wherein the partial state space in which the basic behaviors aregenerated is used in common for all the ‘growth stages’, but the presentinvention is not limited to it. Each ‘growth stage’ may be divided intoportions, and the partial state space in which the basic behaviors aregenerated may be used in common for each of divided portions of each‘growth stage’.

[0197] Furthermore, in the foregoing second embodiment elucidation isgiven on the case wherein the state space for the behavior and motionmodels for each ‘growth stage’ expands in order. However, the presentinvention is not limited to it, but the state space for the behavior andmotion models for each ‘growth stage’ may be reduced in order, or thestate space for the behavior and motion models may be reduced in any ofthe ‘growth stages’ while still expanding.

[0198] (3) Third Mode of Carrying Out the Present Invention

[0199] (3-1) Structure of a Pet Robot 90 in the Third Mode

[0200] The 90 in FIG. 1 shows a pet robot in the third mode of thecarrying out the present invention. The pet robot 90 is configured inthe same way as the pet robot 1 in the first embodiment, except that, inaddition to the growth function described in the first embodiment, thepet robot 90 is, as shown in FIG. 24, provided with a function totransform the behavior patterns (Baby 1, Child 1, Child 2, Young 1˜Young3, Adult 1 Adult 4) in the same ‘growth stage’ as required according tothe history of input operations executed by the user and the history ofthe behaviors and motions of its own, and to retrograde the ‘growthstage (namely, to transform the behavior patterns to those in a ‘growthstage’ of a lower growth level. This function is referred to as‘behavior pattern transform/retrograde function’ hereinafter).

[0201] It means that in the case of the pet robot 90, in addition tocounting each growth element described in FIGS. 13A, 13B, the controller91 (FIG. 2) is so designed as to constantly watches for and countgeneration in respect to a plurality of elements (referred to as‘behavior pattern transform elements’ hereinafter) related topredetermined ‘behavior pattern transformation’, such as ‘saw a colorthe pet robot 90 likes’, ‘played with the ball’, ‘the ball taken away’,and ‘time (during which the pet robot 90 is) left alone’.

[0202] In the case of the present embodiment each of the following, too,is reckoned as a behavior pattern transforming element and countedseparately from the counting of the growth elements: command input withthe use of the sound commander, enforced learning comprising sensorinputs corresponding to actions ’stroke’ and ‘pat’ through the touchsensor 18 (FIG. 2) and the number of successes in performingpredetermined behaviors and motions, sensor inputs not corresponding toactions ‘stroke’ and ‘pat’ through the touch sensor 18, and each of theforegoing growth elements of a given behavior and motion like ‘play withthe ball’.

[0203] The controller 91 then transforms a behavior and motion model tobe used, into another behavior and motion model in the same ‘growthstage’ regardless of ‘growth’ when the total value of accumulated sumsof these behavior pattern transforming elements (referred to as‘integrated experience value of the behavior pattern transformingelemnents’ hereinafter) exceeds a preset threshold value.

[0204] It is the accumulated number of frequencies of embodiment of eachbehavior pattern transforming element that determines which behavior andmotion model of the other behavior and motion models is used as the nextbehavior and motion model. In the case, for example, that the behaviorand motion model used up to the then moment is a behavior and motionmodel (Young 2) of the ‘normal’ behavior patterns for ‘Young’, when theaccumulated sum of frequencies of a behavior pattern transformingelement of the behavior pattern transforming elements that deterioratesa predetermined behavior pattern, such as ‘patted’ or ‘time (throughwhich the pet robot 90 is) left alone, is greater than that of the otherbehavior pattern transforming elements, the behavior and motion model(Young 1) of ‘irritated’ behavior pattern for ‘Young’ is selected. Asopposed to it, when the accumulated sum of frequencies of a behaviorpattern transforming element of the behavior pattern transformingelement that ameliorates a predetermined behavior pattern, such as‘stroked’ or ‘saw a color the pet robot 90 likes’ is greater than thatof the other behavior pattern transforming elements, the behavior andmotion model (Young 3) of a ‘calm’ behavior pattern for ‘Young’ isselected.

[0205] The other behavior models within the same ‘growth stage’ to whichthe currently used behavior and motion model is allowed to transform,are predetermined, namely limited to the behavior and motion modelsconnected by the arrow lines.

[0206] Accordingly, in the case, for example, that a behavior and motionmodel being used is the behavior and motion model (Adult 3) of ‘a bitcalm’ behavior pattern for ‘Adult’, either the behavior and motion model(Adult 2) of ‘a bit wild’ behavior pattern, or the behavior and motionmodel (Adult 4) of a ‘calm’ behavior pattern only can be selected as thenext behavior and motion model, and the behavior and motion model(Adult 1) for an ‘irritated’ behavior pattern can never be used as thenext behavior and motion model.

[0207] Meantime, in addition to each of the foregoing behavior patterntransforming element, the controller 91 constantly watches for and countgeneration in respect to a plurality of predetermined elements relatingto the ‘retrogression of the growth stages’ (referred to as‘retrogressive elements’ hereinafter), such as ‘fell off the table’,‘fell over’, or ‘received a big impact’.

[0208] Then, when the accumulated number of frequencies of embodiment ofany retrogressive element exceeds the threshold value preset for each ofretrogressive element based on the accumulated number of frequencies ofembodiment of each retrogressive element, the controller 91 transforms abehavior and motion model to be used into a behavior and motion model ofa ‘growth stage’ of a lower growth level than the ‘growth model’ of thebehavior and motion model being used up to the then moment.

[0209] In transforming the current ‘growth stage’ to a ‘growth stage’ ofa lower growth level, it is predetermined for each retrogressive elementto what stage the current ‘growth stage’ is retrograded (or, to abehavior and motion model of which ‘growth stage’). In the case of anevent ‘fell off the table, etc.’ by way of example, the ‘growth stage’is retrograded by two stages if the accumulated number of frequencies is‘1’ (one stage for ‘Child’). If the accumulated number of frequencies ofan event ‘fell over’ exceeds the threshold, the growth stage retrogradesby one stage.

[0210] In retrograding the ‘growth stage’ a behavior and motion model isselected at random which is within the ‘growth stages’.

[0211] Accordingly, in the case wherein the behavior and motion modelused up to the then moment was the behavior and motion model (Adult 1)for an ‘aggressive’ motion pattern for ‘Adult’, the behavior and motionmodel (Young 3) of a ‘calm’ behavior pattern for ‘Young’ may be selecteddue to the retrogression.

[0212] As described, the pet robot 90 is designed such that its‘behavior patterns’ transform in order even while not ‘growing’,according to the input history of actions and commands by the user andthe history of behaviors and motions of its own, or shocks, etc. justlike a real animal transforms its behavior patterns, depending upon howit is reared and as if the mind retrograded due to strong shocks, etc.

[0213] (3-2) Processing of Controller 91

[0214] The contents the controller 91 of the pet robot 90 processes canbe divided as shown in FIG. 25 in terms of functions, wherein the unitscorresponding to those in FIG. 4 are assigned the same referencenumerals. The controller 91 is constructed in the same way as that ofthe first embodiment, except for a growth control mechanism unit 92.

[0215] The ┌transforming of the behavior patterns┘ and ┌retrograding ofgrowth stages┘ are made under the control of this growth controlmechanism unit 92.

[0216] In practice the growth control mechanism unit 92 executes, inparallel with control processing on ‘growth’ as in the forgoing firstembodiment, control processing on the transformation of behaviorpatterns with the same ‘growth stage’ and retrogression of the ‘growthstages’ as follows:

[0217] The growth control mechanism unit 92 stores in a memory 90A alist 93A (referred to as ‘first behavior pattern transforming elementlist’ hereinafter) as shown in FIG. 26A wherein the said behaviorpattern transforming elements are defined, chosen from among variousstates based on the state recognition information S20 given from thestate recognition mechanism unit 30 that should be referred to intransforming the behavior patterns within the same growth ‘stage’, and acounter table 93B (referred to as ‘first behavior pattern transformingelement counter table’ hereinafter) shown in FIG. 26B to count each ofthe accumulated number of frequencies of these behavior patterntransforming elements.

[0218] Upon receiving the state recognition information S20 from thestate recognition mechanism unit 30 the growth control mechanism unit 92judges whether or not a state obtained based on the state recognitioninformation S20 is a behavior pattern transforming element based on thefirst behavior pattern transforming element list 93A, and if the stateis found to be a behavior pattern transforming element, thecorresponding counter value (experience value) within the first behaviorpattern transforming element counter table 93B is increased by ‘1’.

[0219] The growth control mechanism unit 92 also stores in a memory 90Aa list 94A (referred to as ‘second behavior pattern transforming elementlist’ hereinafter) as shown in FIG. 27A wherein the said behaviorpattern transforming elements are defined, chosen from among thebehaviors and motions based on the behavior command information S14given from the behavior determining mechanism unit 32 that should bereferred to in transforming the behavior patterns within the same growth‘stage’, and a counter table 94B (referred to as ‘second behaviorpattern transforming element counter table’ hereinafter) shown in FIG.27B to count each of the accumulated numbers of frequencies of thesebehavior pattern transforming elements.

[0220] Upon receiving the behavior command information S14 from thebehavior determining mechanism unit 32 the growth control mechanism unit92 judges whether or not a behavior or motion obtained based on thebehavior command information S14 is a behavior pattern transformingelement based on the second behavior pattern transforming element list94A, and if the behavior or motion is found to be a growth element, thecorresponding counter value (experience value) within the secondbehavior pattern transforming element counter table 94B is increased by‘1’.

[0221] Furthermore, when the values within the first or second behaviorpattern transforming element counter tables 93B, 94B are increased as inthe foregoing, the growth control mechanism unit 92 increases by ‘1’ thecount value of the counter (referred to as ‘behavior patterntransforming integrated experience value counter’ hereinafter) to judgewhether or not a behavior pattern should be transformed within the same‘growth stage’, which is prepared separately from the first and secondbehavior pattern transforming element counter tables 93B, 94B), and thenjudges whether or not the count value of the behavior patterntransforming integrated experience value counter exceeds that preset asa condition to transform the ‘behavior pattern’.

[0222] If the count value of the behavior pattern transformingintegrated experience value counter reaches a count value preset as acondition to transform the ‘behavior pattern’, the growth controlmechanism unit 92 determines that the behavior and motion model shouldbe transformed to which behavior and motion model within the same‘growth stage’ based on each of the count values in the first and secondbehavior pattern transforming element counter tables 93B and 94B, theresult of which is conveyed as transforming command information S22 tothe feeling/instinct modeling unit 31, behavior determining mechanismunit 32 and posture transition mechanism unit 33.

[0223] Consequently the feeling/instinct modeling unit 31 varies theparameter of each of the intensity increase/decrease function 42A˜42G tothe value of the designated behavior and motion model based on thetransforming command information S22. Also, the behavior determiningmechanism unit 32 transforms a behavior model to be used into theappointed behavior and motion model based on the transforming commandinformation S22.

[0224] The posture transition mechanism unit 33 changes the settingbased on the transforming command information S22 such that a directedarc or self-acting arc corresponding to the appointed behavior andmotion model is selected in such a case that one of the directed arcs orself-acting arcs must be selected from among the directed arcs andself-acting arcs corresponding to a plurality of behavior and motionmodels.

[0225] In this way the growth control mechanism unit 92 controls thetransforming of a behavior pattern within the same ‘growth stage’according to the history of actions made by the user, operation input ofcommands with the use of the sound commander, or the history ofbehaviors and motions of its own.

[0226] Meantime, the growth control mechanism unit 92 stores in thememory 90A a list 95A (referred to as ‘retrogressive element list’hereinafter) as in FIG. 28A wherein the said retrogressive elements aredefined, chosen from among various states based on the state recognitioninformation S20 given from the state recognition mechanism unit 30 thatshould be referred to in retrograding the ‘growth state’, and a countertable 95B (referred to as ‘retrogressive element counter table’hereinafter) shown in FIG. 28B to count each of the accumulated numbersof frequencies of these retrogressive elements.

[0227] Upon receiving the state recognition information S20 from thestate recognition mechanism unit 30 the growth control mechanism unit 92judges whether or not the state obtained based on the retrogressiveelement list 95A, and if the state is found to be a retrogressiveelement, the corresponding counter value (experience value) within theretrogressive element counter table 95 is increased by ‘1’.

[0228] Furthermore, the growth control mechanism unit 92 stores in thememory 90A a list 96 (referred to as ‘retrogressive condition and stagenumber list) as in FIG. 29, containing the threshold value preset foreach of the retrogressive elements and the number of retrogressivestages of the ‘growth stage’ in the case that the accumulated sum offrequencies of the retrogressive element exceeds the threshold value.

[0229] When the count value of any of the retrogressive elements withinthe retrogressive element counter table 95B is increased, the growthcontrol mechanism unit 92 judges whether or not the count value of theretrogressive element exceeds the threshold value preset for theretrogressive element, referring to the retrogressive condition andstage number list 96.

[0230] If the count value of the retrogressive list exceeds thecorresponding threshold value, the growth control mechanism unit 92determines at random based on the retrogressive condition and stagenumber list 96 that the behavior and motion model should be transformedinto which behavior and motion model in a ‘growth state’ lower by asmany stages as predetermined for the generation element of the behaviorand motion model, the result of which is conveyed as the transformingcommand information S22 to the feeling/instinct modeling unit 31,behavior determining mechanism unit 32 and posture transition mechanismunit 33.

[0231] Consequently the feeling/instinct modeling unit 31 transforms theparameter of each of the increase/decrease functions 42A˜42G to thevalue of the appointed behavior and motion model based on thetransforming command information S22. The behavior determining mechanismunit 32 transforms a behavior model to be used into the appointedbehavior and model based on the transforming command information S22.Furthermore, the posture transition mechanism unit 33 then changes thesetting such that a directed arc or self-acting arc corresponding to theappointed behavior and motion model is selected in such a case that anyof directed arc or self-acting arc must be selected from among thedirected arcs and self-acting arcs corresponding to a plurality ofbehavior and motion models.

[0232] As described in the foregoing, the growth control mechanism unit92 controls the retrograding of the ‘growth stages’ based on theexternal information signal S2 from the external sensor 19 and theinternal information signal S1 from the internal sensor 15.

[0233] (3-3) Behavior Pattern Transform Processing Procedure RT2 andRetrogressive Processing Procedure RT3

[0234] The growth control mechanism unit 92 executes the processing ofthe behavior pattern transforming in the same ‘growth stage’ inaccordance with the behavior pattern transform processing procedure RT2shown in the FIG. 30.

[0235] That is to say, the growth control mechanism unit 92 startsexecuting the behavior pattern transform processing procedure RT2 at thestep SP10 ever time the behavior and motion model is transformed, andjudges at the subsequent step SP11 whether or not state recognitioninformation S10 is given from the state recognition mechanism unit 30.

[0236] If a negative result is obtained at this step SP11, the growthcontrol mechanism unit 92 proceeds to the step SP12 and judges whetheror not behavior determining information S14 is given from the behaviordetermining mechanism unit 32. If a negative result is obtained at thisstep SP12, the growth control mechanism unit 92 returns to the step SP11and repeats an SP11-SP12-SP11 loop until an affirmative result isobtained either at the step SP11 or SP12.

[0237] When an affirmative result is obtained in due course at the stepSP11, the growth control mechanism unit 92 proceeds to the step SP13 andjudges whether or not the state to be obtained based on the staterecognition information S10 given from the state recognition mechanismunit 30 is a behavior pattern transforming element.

[0238] If a negative result is obtained at this step SP13, the growthcontrol mechanism unit 92 returns to the step SP11, while if anaffirmative result is obtained, the growth control mechanism unit 92proceeds to the step SP15 and increases by ‘1’ the corresponding countvalue in the first behavior pattern transforming element counter table93B (FIG. 26B) and the count value of the integrated experience valuecounter for the behavior pattern transforming respectively.

[0239] If an affirmative result is obtained at the step SP12, the growthcontrol mechanism unit 92 proceeds to the step SP14 and judges whetheror not a behavior or motion to be obtained based on the behaviordetermining information S14 given from the behavior determiningmechanism unit 32 is a behavior pattern transforming element.

[0240] If, however, a negative result is obtained at this step SP14, thegrowth control mechanism unit 92 returns to the step SP11, while if anaffirmative result is obtained, the growth control mechanism unit 92proceeds to the step SP15 and increases by ‘1’ the corresponding countvalue in the second behavior pattern transforming element counter table94B (FIG. 27B) and the count value of the behavior pattern transformingintegrated experience value counter respectively.

[0241] After terminating the processing at the step SP15 the growthcontrol mechanism unit 92 proceeds to the step SP16 and judges whetheror not the count value of the behavior pattern transforming integratedexperience value counter reaches the count value preset as a conditionto transform the current behavior and motion model.

[0242] If a negative result is obtained at this step SP16, the growthcontrol mechanism unit 92 returns to the step SP11, while if anaffirmative result is obtained, the growth control mechanism unit 92proceeds to the step SP17 and determines that the behavior and motionmodel should be transformed to which behavior and motion model in thesame ‘growth stage, the result of which is conveyed to thefeeling/instinct modeling unit 31, behavior determining mechanism unit32 and posture transition mechanism unit 33.

[0243] Furthermore, the growth control mechanism unit 92 proceeds to thestep SP18 and resets the first and second behavior pattern transformingelement counter tables 93B, 94B such that all the count values in thefirst and second behavior pattern transforming element counter tables93B, 94B becomes ‘0’. The growth control mechanism unit 92 then proceedsto the step SP19 and terminates the behavior pattern transformingprocessing procedure RT2.

[0244] Meanwhile, the growth control mechanism unit 92 executes thecontrol processing of the retrograding of the ‘growth stage’ accordingto the retrogressive processing procedure RT3 shown in FIG. 31, inparallel with the foregoing.

[0245] That is, the growth control mechanism unit 92 starts executingthe retrogressive processing procedure RT3 every time the behavior andmotion model is transformed, and then proceeds to the subsequent stepSP21 and stands by for state recognition information S10 to be suppliedfrom the state recognition mechanism unit 30.

[0246] When an affirmative result is obtained at the step SP21 in duecourse, the growth control mechanism unit 92 proceeds to the step SP22and judges whether or not the state to be obtained based on the staterecognition information S10 given from the state recognition mechanismunit 30 is a retrogressive element.

[0247] If a negative result is obtained at the step SP22, the growthcontrol mechanism unit 92 returns to the step SP21, while, if anaffirmative result is obtained, the growth control mechanism unit 92proceeds to the step SP23 and increases by ‘1’ the corresponding countvalue in the retrogressive element counter table 95B (FIG. 28B).

[0248] The growth control mechanism unit 92 then proceeds to the stepSP24 and judges whether or not the count value of the retrogressiveelement reaches the count value preset as a retrogressive condition,referring to the retrogressive condition and stage number list 96 (FIG.29).

[0249] If a negative result is obtained at this step SP24, the growthcontrol mechanism unit 92 returns to the step SP21, while if anaffirmative result is obtained, the growth control mechanism unit 92proceeds to the step SP25 and determines at random that the behavior andmotion model should be transformed to which behavior and motion model inthe ‘growth stage’ of a growth level lower by as many stages as presetfor the retrogressive element, the result of which is conveyed to thefeeling/instinct modeling unit 31, behavior determining mechanism unit32 and posture transition mechanism unit 33.

[0250] Furthermore, the growth control mechanism unit 92 then proceedsto the step SP26 and resets the retrogressive element counter table 95Bsuch that all the count values in the retrogressive element countertable 95B becomes ‘0’. Then the growth control mechanism unit 92proceeds to the step SP27 and terminates the retrogressive processingprocedure RT3.

[0251] (3-4) Operations and Effects in this Mode of Carrying out theInvention

[0252] Constructed as described in the foregoing, the pet robot 90 growsby stages: from a stage where its behaviors and motions are childish toa stage where its behaviors and motions are adultlike, as if it ‘grew’like a real animal as time goes by.

[0253] The pet robot 90 transforms its behavior patterns not only in‘growing’, depending upon how the user has got along with it and thesurroundings it has been put in, and according to the history of its ownbehaviors and motions, etc., but transforms its behaviors and patternsgradually on other occasions, depending upon how the user gets alongwith it and the surroundings it has been put in, and according to thehistory of its own behaviors and motions. Meanwhile, it is also possiblethat the retrogression of the growth level occurs when the pet robot 90receives strong shocks, for example.

[0254] Accordingly, with the pet robot 90 not only its behaviorspatterns can be transformed but its growth level retrograded, dependingupon how the user gets along with it and the surroundings it has beenput in, and according to the history of its own behaviors and motions,with no regard to ‘growing’, so that it may retain the user's interestand give him/her a larger sense of affinity and satisfaction.

[0255] Constructed as described heretofore, with the pet robot 90 notonly its behaviors patterns can be transformed but its growth levelretrograded, depending upon how the user gets along with it and thesurroundings it has been put in, and according to the history of its ownbehaviors and motions, with no regard to ‘growing’, so that it mayretain the user's interest and give him/her a larger sense of affinityand satisfaction. Consequently a pet robot may be realized whoseamusement quality (entertainingness) is greatly increased.

[0256] (3-5) Other Modes of Carrying Out the Present Invention

[0257] In the foregoing third mode of carrying out the presentinvention, elucidation is given on the case wherein the presentinvention is applied to the four-footed walking pet robot 90. However,the present invention is not limited to it, but may be applied widely torobots in a variety of other configurations. It may also be applied tomoving characters, etc. on the monitor screen by means of computergraphics.

[0258] In the case of the foregoing third mode of carrying out thepresent invention, elucidation is given on the case wherein the behaviorand/or motion generation means to create behaviors and motions based onthe behavior and motion models comprises the controller 10, actuators 21₁˜21 _(n), speaker 20, and the LED placed at the position of an eye,etc. However, the present invention is not limited to it, but may beapplicable to a variety of other configurations according to the mode ofa robot to which the present invention is applied.

[0259] Also, in the case of the foregoing third mode of carrying out thepresent invention, elucidation is given on the case wherein one and thesame controller 90 (growth control mechanism unit 92) comprises thefirst transforming means to transform a behavior and motion model tobehavior and motion models of a higher growth level in succession, andthe second transforming means to transform a behavior and motion modelto another behavior and motion model of an equal or a lower growth levelbased on at least one of the input history from the outside and thehistory of the behavior and motions of its own. However, the presentinvention is not limited to it, but these transforming means may beformed in separate units.

[0260] Furthermore, in the case of the foregoing third mode of carryingout the present invention, elucidation is given on the case wherein thebehavior patterns and growth levels are transformed based on both theinput history from the outside and the history of behaviors and motionsof its own. However, the present invention is not limited to it, but thebehavior patterns and growth levels of the pet robot 1 may betransformed by other timings than ‘growth’ based on either of the inputhistory from the outside or the history of the behaviors and motions ofits own, or by the combination of other elements in addition to theinput history from the outside or the history of the behaviors andmotions of its own. Furthermore, the behavior patterns and growth levelsmay be transformed based on either the history of its own behaviors orthe history of its own motions.

[0261] Furthermore, in the case of the foregoing third mode of carryingout the present invention, elucidation is given on the case wherein thepet robot 90 is designed to ‘grow’ by stages. However, the presentinvention is not limited to it, but the pet robot 90 may be designed to‘grow’ with no stages by detecting the state of growth elements or bygradually varying the control parameter values every time the behavioror motion of the growth element is performed.

[0262] Furthermore, in the case of the foregoing third mode of carryingout the present invention, elucidation is given on the case wherein thepet robot 90 is designed to ‘grow’ or ‘retrograde’ in four stages:‘Baby’, ‘Child’, ‘Young’, and ‘Adult’. However, the present invention isnot limited to it, but the number of ‘growth stages’ may be set to othernumbers than four (4).

[0263] In this case, as with the growth stage model shown in FIG. 32 byway of example, when the transition enable conditions are satisfied at acertain cell 97, ‘growth’, ‘retrogression’, and ‘the transforming of abehavior pattern’ may be carried out in such a way that the pet robot 90may transits to a cell 97 ‘equal’ to, or ‘lower’ or ‘higher than its owngrowth level.

[0264] Furthermore, in the case of the foregoing third mode of carryingout the present invention, elucidation is given on the case wherein thehistory of contact inputs through the touch sensor 18, photographs byCCD camera, and command sound inputs using sound commands, etc. areapplied as input history from the outside. However, the presentinvention is not limited to it, but other means in addition to theabove, or other means only may be used to make the input history.

[0265] Furthermore, in the case of the foregoing third mode of carryingout the present invention, elucidation is given on the case wherein aplurality of behavior and motion models are prepared for each ‘growthstage’ after ‘Child’. However, the present invention is not limited toit, but the only behavior and motion model may be prepared for each‘growth stage’.

[0266] Furthermore, in the case of the foregoing third mode of carryingout the present invention, elucidation is given on the case wherein thefour (4) items of ‘walking state’, ‘motion’, ‘behavior’, and ‘sound’ aredesignated as variables to vary along with ‘growing’. However, thepresent invention is not limited to it, but other items or elements maybe used as variables to vary along with ‘growing’.

[0267] Furthermore, in the case of the foregoing third mode of carryingout the present invention, elucidation is given on the case wherein thebehavior patterns of the pet robot 90 are transformed (behavior andmotion models are transformed) based on the behavior pattern transformintegrated experience value calculated based on the accumulated sum offrequencies of each behavior pattern transforming element. However, thepresent invention is not limited to it, but the timing to transform thebehavior patterns of the pet robot 90 may be determined by otherconditions than this.

[0268] Similarly in the case of the foregoing third mode of carrying outthe present invention, elucidation is given on the case wherein a‘growth stage’ is retrograded based on the accumulated sum offrequencies of each retrogressive element. However, the presentinvention is not limited to it, but the timing to retrograde a ‘growthstage’ of the pet robot 90 may be determined by other conditions thanthis.

[0269] Furthermore, in the case of the foregoing third mode of carryingout the present invention, elucidation is given on the case whereintransition is allowed only among the behavior and motion models in FIG.24 connected by the arrow lines in transforming a behavior and motionmodel within the same ‘growth stage’. However, the present invention isnot limited to it, but transition may be allowed among the behavior andmotion models not connected by the arrow lines.

[0270] Furthermore, in the case of the foregoing third mode of carryingout the present invention, elucidation is given on the case wherein theinput history from the outside only is used as a retrogressive element.However, the present invention is not limited to it, but it may be alsoconceivable to use as a retrogressive element, the history of thebehaviors and motions of its own in addition to it, or the history ofthe behaviors and motions of its own only.

[0271] (4) Fourth Mode of Carrying Out the Present Invention

(4-1) Structure of a Pet Robot in the Fourth Embodiment

[0272] In FIG. 1 the ‘100’ is a pet robot in whole in a fourth mode ofcarrying out the present invention, which is constructed in the same wayas the pet robot 1 is in the first mode of carrying out the presentinvention, except for two points: that it is provided with a pluralityof behavior and motion models by which to determine the next behaviorfor each behavior pattern (Baby 1, Child 1, Child 2, Young 1˜Young 3,Adult 1˜Adult 4 in FIG. 3) for each ‘growth stage’, and that it has afunction to transform the number of frequencies of appearance of abehavior and motion as physically exerted by the user (This function isreferred to as ‘learning function’ hereinafter).

[0273] This means, in the case of the pet robot 100 the contents acontroller 101 (FIG. 2) processes are divided as shown in FIG. 33,wherein the same reference numeral is assigned to the unit correspondingto one in FIG. 4.

[0274] The behavior determining mechanism unit 102 of the controller 101has an individual behavior and motion model 103 ₁˜103 _(n) for each itemof several conditions preselected, such as ‘deal with the ball’,‘autonomous detection’, ‘feeling expression’, and ‘avoid an obstacle’,etc. Each individual behavior and motion model 103 ₁˜103 _(n) is thesame as described in FIG. 6 and FIG. 7.

[0275] The behavior determining mechanism unit 102 first determines thenext behavior using a behavior and motion model corresponding to eachbehavior pattern on such occasions as when state recognition informationS10 is given from the state recognition mechanism unit 30, or when agiven period of time has elapsed since the last behavior appeared, andthen selects a behavior from among the determined behaviors, using abehavior and model 103 ₁˜103 _(n) with a high priority in predeterminedorder according to the then recognition results, etc. obtained by meansof the state recognition mechanism unit 30, which (the selectedbehavior) is conveyed as behavior determining information S14 to thefeeling/instinct modeling unit 31, posture transition mechanism unit 33,and learning control mechanism unit 104 respectively.

[0276] In this manner the pet robot 100 is designed to be capable ofembodying a variety of behaviors and motions from the same input byusing a plurality of behavior and motion models 103 ₁˜103 _(n) for eachbehavior pattern.

[0277] Meantime, the state recognition mechanism unit 30 recognizesevents ‘stroked’ or ‘patted’ based on the pressure detection signal S1C(FIG. 2) given from the touch sensor 18, the result of which is conveyedto the learning control mechanism unit 104.

[0278] At this time the learning control mechanism unit 104 knows thepresent and past behaviors of the pet robot 100 based on the behaviordetermining information S14 given from the behavior determiningmechanism 102. Then, given the recognition result from the staterecognition mechanism unit 30 that the pet robot 100 has been ‘stroked’while embodying behaviors, the learning control mechanism unit 104conveys this result to the behavior determining mechanism 102.

[0279] Thus, based on this notification the behavior determiningmechanism 102 increases by the predetermined value the transitionprobability corresponding to the behavior or motion then outputted,which is on the state transition table 50 (FIG. 7) of the nodeNDA_(A0)˜ND_(An) (FIG. 6) selected just before it for each behavior andmotion model 103 ₁˜103 _(n) of the corresponding behavior pattern, whiledecreases by the predetermined value the other transition probabilitieson the same line in response to the former, so that the total sumbecomes 100 [%].

[0280] Meanwhile, given the recognition result that the pet robot hasbeen ‘stroked’ while embodying a behavior from the state recognitionmechanism unit 30, the learning control mechanism unit 104 conveys thisresult to the behavior determining mechanism 102.

[0281] Thus, based on this notification the behavior determiningmechanism 102 increases by the predetermined value the transitionprobability corresponding to the behavior or motion then outputted,which is on the state transition table 50 (FIG. 7) of the nodeND_(A0)˜ND_(An) (FIG. 6) selected just before it for each behavior andmotion model 103 ₁˜103 _(n) of the corresponding behavior pattern, whiledecreases by the predetermined value the other transition probabilitieson the same line in response to the former, so that the total sumbecomes 100 [%].

[0282] Controlled as described in the foregoing, with an action’stroked’ exerted the transition probability corresponding to thataction increases, thereby making it easier for the pet robot 100 toembody that action, and with an action ‘patted’ exerted the transitionprobability corresponding to that action decreases, thereby making itharder for the pet robot 100 to embody that action. In this way it ispossible to have the pet robot 100 transform its behaviors as if itcould behave like a real animal as a result of learning the disciplinesby the keeper.

[0283] Consequently the pet robot 100 is capable of transforming itsbehaviors and motions by achieving learning as physically exerted by theuser.

[0284] Furthermore, in the case of the pet robot 100 thus constructedthe learning speed varies in respect to each behavior pattern (Baby 1,Child 1, Child 2, Young 1˜Young 3, Adult 1˜Adult 4) and each behaviorand motion model 103 ₁˜103 _(n) of each behavior pattern.

[0285] It means that the behavior determining mechanism 102 comprises ina memory 101A (FIG. 2) a table regulating the learning speed for each ofthe behavior and motion models 103 ₁˜103 _(n) (referred to as ‘learningspeed table’ hereinafter).

[0286] If the notification is given from the learning control mechanismunit 104 that the pet robot 100 has been ‘stroked’ while embodying abehavior, the behavior determining mechanism 102 increases by as muchvalue as specified by the learning speed table 105 the transitionprobability corresponding to the then outputted behavior or motion onthe state transition table 50 (FIG. 7) of the corresponding nodeND_(A0)˜ND_(An) (FIG. 6) in respect to each behavior and motion models103 ₁˜103 _(n) of the corresponding behavior pattern, while decreasesthe values of the other transition probabilities on the same line inresponse to the former.

[0287] For example, if the then behavior pattern is a ‘normal’ behaviorpattern (Young 2) for ‘Young’, the behavior determining mechanism unit102 increases by ‘5’ only the corresponding transition probability onthe state transition table 50 of the corresponding node ND_(A0)˜ND_(An)in respect to the behavior and motion model 103 ₁ for ‘deal with theball’, by ‘2’ only the corresponding transition probability on the statetransition table 50 of the corresponding node ND_(A0)˜ND_(An) in respectto the behavior and motion model 103 ₂ for ‘autonomous detection’, andby ‘1’ only the corresponding transition probability on the statetransition table 50 of the corresponding node ND_(A0)˜ND_(An) in respectto the behavior and motion model 103 ₃ for ‘battery management’.

[0288] Whereas, if the notification is given from the learning controlmechanism unit 104 that the pet robot 100 has been ‘patted’ whileembodying a behavior, the behavior determining mechanism unit 102decreases by as much value as specified by the learning speed table 105the transition probability corresponding to the then output behavior ormotion on the state transition table 50 (FIG. 7) of the correspondingnode ND_(A0)˜ND_(An) (FIG. 6) in respect to each behavior and motionmodel 103 ₁˜103 _(n) of the corresponding behavior pattern whileincreases the values of the other transition probabilities on the sameline in response to the former.

[0289] For example, if the then behavior pattern is a ‘aggressive’behavior pattern (Adult 1) for ‘Adult’, the behavior determiningmechanism unit 102 decreases by ‘2’ only the corresponding transitionprobability on the state transition table 50 of the corresponding nodeND_(A0)˜ND_(An) in respect to the behavior and motion model 103 ₁ for‘deal with the ball’, by ‘6’ only the corresponding transitionprobability on the state transition table 50 of the corresponding nodeND_(A0)˜ND_(An) in respect to the behavior and motion model 103 ₂ for‘autonomous detection’, and by ‘0’ (no transforming in the transitionprobability in this case) the corresponding transition probability onthe state transition table 50 of the corresponding node ND_(A0)˜ND_(An)in respect to the behavior and motion model 103 _(n) for ‘batterymanagement’.

[0290] As described heretofore, with the pet robot 100 the correspondingtransition probability on the state transition table 50 of thecorresponding ND_(A0)˜ND_(An) is varied, in response to the physicalinfluence from the user, by changing the speed of learning of eachbehavior pattern as well as behavior and motion model 103 ₁˜103 _(n), inparticular, of each behavior pattern.

[0291] (4-2) Operations and Effects in this Embodiment

[0292] Configured as described heretofore, with the pet robot 100 thespeed of learning is varied for each behavior and motion model 103 ₁˜103_(n) according to behavior patterns (Baby 1, Child 1, Child 2, Young1˜Young 3, Adult 1˜Adult 4).

[0293] Accordingly the pet robot 100 is capable of representing avariety of individualities by combining ‘growth’ and ‘learning’.

[0294] Thanks to such a configuration, a variety of individualities maybe represented in combination of ‘growth’ and ‘learning’ byincorporating the learning function into the pet robot 100, preparing aplurality of behavior and motion models 103 ₁˜103 _(n) for each behaviorpattern, and varying the speed of learning for each of behavior andmotion models 103 ₁˜103 _(n) according to a behavior pattern. Thus a petrobot can be realized that may offer a greatly enhanced quality ofamusement.

[0295] (4-3) Other Modes of Carrying Out the Present Invention

[0296] In the foregoing fourth embodiment, elucidation is given on thecase wherein the present invention is applied to four-footed walking petrobots as illustrated in FIG. 1.

[0297] However, the present invention is not limited to it, but may beapplied widely to a variety of other robots capable of ‘giving’ and‘receiving’.

[0298] Also, in the foregoing fourth embodiment, elucidation is given onthe case wherein a plurality of behavior and motion models 103 ₁˜103_(n) are prepared for each behavior pattern and a different speed oflearning is set for each of the behavior and motion models 103 ₁˜103_(n). However, the present invention is not limited to it, but adifferent speed of learning may be set for each behavior pattern even inthe case that the only behavior and motion model is prepared for eachbehavior pattern as in the case of the first embodiment for example.

[0299] Furthermore, in the foregoing embodiment, elucidation is given onthe case wherein the speed of learning may be varied for each of thebehavior and motion models 103 ₁˜103 _(n). However, the presentinvention is not limited to it, but items to learn may be varied foreach of the behavior and motion models 103 ₁˜103 _(n), so that thenumber of frequencies of an embodiment varies by learning an item(event) ‘kick the ball’ for example, in a certain behavior and motionmodel 103 ₁˜103 _(n) (that is, the transition probability increases ordecreases) but that it does not vary in the other behavior and motionmodel 103 ₁˜103 _(n).

[0300] Furthermore, in the foregoing embodiment, elucidation is given onthe case wherein the frequency of embodiment of a certain behavior isvaried by means of learning. However, the present invention is notlimited to it, but a learning function may be provided for a certainbehavior and motion, for example, that varies control parameters so thata behavior or motion is conducted in a much better way thanks to thelearning acquired by the physical influence from the user. (Example: alearning function capable of interchanging several sets of controlparameters prepared for an event ‘walking’ for example so that theparameters applied to a ‘poor way of walking’ may be changed to those ofa ‘better way of walking’ by the influence from the user, such as‘stroke’ or ‘pat’, and vice versa.)

[0301] Industrial Applicability

[0302] The present invention may be applied to entertaining robots suchas pet robots.

1. A robot characterized by; behavior and/or motion generating means forgenerating behaviors and/or motions based on behavior and/or motionmodels, and behavior and/or motion model transforming means fortransforming said behavior and/or motion models into said behaviorand/or motion models of a higher growth level at a given timing based onat least one of the input history from the outside and the history ofthe own behaviors and/or motions of its own.
 2. The robot of claim 1characterized in that; said behavior and/or motion model transformingmeans transforms said behavior and/or motion models by stages.
 3. Therobot of claim 2 characterized in that; a plural number of said behaviorand/or motion models are provided for each of said stages, said behaviorand/or motion transforming means, selects, in transforming saidbehaviors and/or motions, a behavior and/or motion model into whichtransition is made, from among said behavior and/or motion models forthe next stage based on at least one of the input history from theoutside and the history of the behaviors and/or motions of its own. 4.The robot of claim 2 characterized in that; said behavior and/or motiontransforming means, uses part of said behavior and/or motion models incommon for each of said stages.
 5. The robot of claim 2 characterized inthat; said behavior and/or motion transforming means, transforms saidbehavior and/or motion models by changing a state space, either enlargedor reduced, to be used for generation of said behaviors and/or motionsfrom among said behavior and motion models according to each of saidstages.
 6. The robot of claim 1 characterized in that; said behaviorand/or motion models consist of state nodes and state transition modelsrepresenting behaviors and/or motions in terms of arcs.
 7. The robot ofclaim 6 characterized in that; said behavior and/or motion transformingmeans selects said arc based on the probability set f or each of aplurality of said arcs and/or weighting coefficients.
 8. A robotcharacterized in comprising; behavior and/or motion generation means forgenerating behaviors and/or motions based on behavior and/or motionmodels, first transforming means for transforming in order said behaviorand/or motion models into said behavior and/or motion models of a highergrowth level according to first given conditions, and secondtransforming means for transforming said behavior and/or motion modelsinto other behavior and/or motion models of an equal or a lower growthlevel according to second given conditions based on at least one of theinput history from the outside and the history of the behaviors and/ormotions of its own.
 9. The robot of claim 6 characterized in that; oneor a plurality of said behavior and/or motion models are prepared foreach of said growth levels, said second transforming means selects, intransforming said behavior and motion models, one of said behaviorand/or motion models from among said behavior and/or motion models ofthe corresponding growth level, which is transformed into a suitablebehavior and/or motion model.
 10. A robot characterized in comprising;behavior and/or motion generation means for generating behaviors and/ormotions based on the behavior and/or motion models, behavior and/ormotion transforming means for transforming said behavior and/or motionmodels into said behavior and/or motion models of a higher growth levelat a given timing based on the appraisal results by appraising its ownbehaviors based on given-appraisal functions.
 11. A robot having aplurality of behavior and/or motion models of a plurality of behaviorpatterns, comprising; behavior and/or motion generating means forgenerating a behavior and/or motion based on each of said behaviorand/or motion models of corresponding said behavior pattern, andtransforming means for transforming each of said behavior and/or motionmodels of corresponding said behavior pattern according to influencefrom the outside, said transforming means, transforms each of saidbehavior and/or motion models of corresponding said behavior pattern bydifferent regulations preset for each of said behavior and/or motionmodels.
 12. A control method of a robot characterized in comprising;first step wherein behaviors and/or motions are generated based on thebehavior and/or motion models, and second step wherein said behaviorand/or motion models are transformed into behavior and/or motion modelsof a higher growth level at a given timing based on at least one of theinput history from the outside and the history of the behaviors andmotions of its own.
 13. The control method of a robot of claim 12characterized by; said second step wherein, said behavior and motionmodels are transformed by stages.
 14. The control method of a robot ofclaim 13 characterized in that; a plurality of said behavior and/ormotion models are prepared for each of said stage, said second stepwherein, one of said behavior and/or motion models, to which transitionis made next, is selected from among said behavior and/or motion modelswithin said stage, based on at least one of the input history from theoutside and the history of the behaviors and motions of its own.
 15. Thecontrol method of a robot of claim 13 characterized in that; said secondstep wherein, parts of said behavior and/or motion models are used incommon for each of said stages.
 16. The control method of a robot ofclaim 13 characterized in that; said second step wherein, said behaviorand/or motion models are transformed by altering a state space, eitherenlarged or reduced, to be used for generation of behaviors and motionsof said behavior and/or motion models according to each of said stages.17. The control method of a robot of claim 12 characterized in that;said behavior and/or motion models consist of state nodes and statetransition models representing behaviors and/or motions in terms ofarcs.
 18. The control method of a robot of claim 17 characterized inthat; said second step, selects an arc from among said arcs based on aprobability and/or weighting coefficient preset for each of a pluralityof said arcs.
 19. A control method of a robot characterized incomprising; first step wherein behaviors and/or motions are generatedbased on behavior and motion models, and second step wherein saidbehavior and/or motion models are transformed into behavior and/ormotion models of a higher growth level in order according to first givenconditions, and wherein said behavior and/or motion models aretransformed into different behavior and/or motion models of an equal ora lower growth level according to second given conditions based on atleast one of the input history from the outside and the history of thebehaviors and motions of its own.
 20. The control method of a robot ofclaim 19 characterized in that; one or a plurality of said behaviorand/or motion models are prepared for each of said stage levels, saidsecond step wherein, one of said behavior and/or motion models isselected from among said behavior and/or motion models of correspondingsaid growth level.
 21. A control method of a robot characterized inthat; first step wherein behaviors and/or motions are generated based onbehavior and/or motion models, and second step wherein said behavior andmotion models are transformed into said behavior and motion models of ahigher growth level at a given timing based on the appraisal resultsobtained by appraising its own behaviors based on given appraisalfunctions.
 22. A control method of a robot characterized in that; aplurality of behavior and/or motion models are provided for a pluralityof behavior models, and comprising; first step wherein behaviors and/ormotions are generated based on said behavior and/or motion models of thecorresponding behavior patterns second step wherein each of saidbehavior and/or motion models of corresponding said behavior pattern istransformed responding to the influence from the second step whereinsaid behavior and/or motion models of the corresponding said behaviorpatterns are transformed according to the influence from the outside,and characterized by; said second step wherein, each of said behaviorand/or motion models of the corresponding behaviors patterns is alteredby different regulations preset for each of said behavior and/or motionmodels.